Lead Applied Scientist, Marketing
RemoteMontevideo, Montevideo Department, Uruguay
Job Summary
Own the full data science engine for the Insurance vertical, framing business problems directly with stakeholders to build and validate models that drive measurable ROAS and lead quality improvements. Deliver buying models maintaining positive returns while handing the ML-engineering last mile to your partner, then stay engaged through deployment, monitoring, and performance analysis across Messaging, Funnels, and Content strategies. Identify net-new modeling opportunities and establish trusted partnerships with vertical stakeholders to produce validated, documented outputs with low correction burdens. Focus on multi-armed bandit and reinforcement learning techniques within the AWS cloud to accelerate revenue growth for our global team.
Required Qualifications
- Proven experience in digital marketing, performance marketing, or the leadgen industry
- Strong modeling fundamentals: the ability to build effective models that drive business impact
- Multi-year, hands-on experience building and deploying ML solutions in the AWS cloud
- Hands-on experience across core technique areas: multi-armed bandit / reinforcement learning, recommendation and ranking systems (content-based, collaborative filtering, hybrid), funnel and monetization optimization, LTV modeling
- Expert Python and SQL
- 5+ years in a hands-on, in-the-weeds applied data science role delivering measurable business impact
Desired Qualifications
- Building adtech algorithms and supporting user acquisition or paid media modeling (highly desired)
- Sophisticated ML at companies where paid digital media is core to the business model
- Creative embeddings work: incorporating embeddings of creatives, videos, headlines, and search into paid media models
- Insurance domain experience
- Creating state-of-the-art Ad Ranking algorithms
- Modeling against ad-platform data points (Google, Meta, native)
- LLMs / deep learning applied to personalization or content
- Familiarity with Looker
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